Vaccine trials can be large (sample size) and long (follow-up duration) with there being multiple factors contributing to the risk of an under or over powered trial. Some main risk factors include:

• Incidence of the disease and the prevalence of individuals at heightened risk of getting infected or acquiring the disease. The incidence/prevalence rate could also be time varying;
• Assumption on vaccine efficacy. An ambitious assumption might lead to underpowering (sample size less than required) while a conservative assumption leads to an overpowered trial;
• Incorrect estimation of optimal follow-up time; and
• In early phase vaccine trials, selection of subtherapeutic or overly toxic dose or dose regimen.

We advocate and can support the use of properly planned adaptive designs to mitigate such risk through blinded/unblinded multiple interim looks for such long and large clinical trials. Some novel statistical techniques used for achieving this are:

• Group sequential designs allowing for early stopping for futility or efficacy;
• Adaptive blinded and unblinded sample size re-estimation at one or more interim looks. This also includes adaptively increasing the follow-up duration;
• Designs allowing for population enrichment;
• Starting the trial with multiple doses and allowing for adaptive dose selection at an interim for eventual confirmation;
• Bayesian sequential designs for rapidly establishing proof-of-concept for vaccine efficacy and safety;
• Seamless Phase-2/3 designs under the Bayesian framework.

We understand the challenge of setting up a trial with multiple interim looks as proper conduct of interim analyses can increase the operational burden. Depending on the size and duration of the trial, we can help in implementing statistical frameworks under which interim analyses can be performed with minimal impact on operations while providing robust and reliable data to make interim decisions with confidence.